FIELD OF THE INVENTION
[0001] The present invention relates to a cognitive performance determination apparatus,
a cognitive performance determination method, as well as to a computer program element
and a computer readable medium.
BACKGROUND OF THE INVENTION
[0002] Practice effect with respect to neuropsychological assessments is when a person's
performance improves on a task where this is not the primary outcome measure. For
some neuropsychological assessments it is recommended to first train and familiarize
the person (patient) with a certain cognitive test to ensure that they have reached
their performance plateau before the actual assessment of cognitive functioning will
take place (see for example:
Wesnes, K., & Pincock, C. (2002). Practice effects on cognitive tasks: a major problem?.
The Lancet Neurology, 1(8), 473). That way the person (patient) and the assessor can be sure that the person is at
their absolute peak and does not show a practice effect in the assessment (or in subsequent
assessments), i.e. shows unwanted improvement in a certain cognitive test. A test
consists of one or more trials. Each trial consists of a fixed logical order of events,
often involving the presentation of a stimulus and processing the response to said
stimulus. If a test has more than one trial, the trials can have the same difficulty
but not necessarily. A test is for example the Rey Auditory Verbal Learning Test (RAVLT),
which consists of 5 trials of repeating words. (Multiple tests are known as a neuropsychological
assessment or a test battery).
[0003] Practice effects, also referred to as retest or learning effects, are improvements
in performance after repeated exposure to test materials (Jutten et al., 2020). Especially
in repeated longitudinal assessment this is important, (i.e., Fig. 1 by
Bartels, C., Wegrzyn, M., Wiedl, A., Ackermann, V., & Ehrenreich, H. (2010). Practice
effects in healthy adults: a longitudinal study on frequent repetitive cognitive testing.
BMC neuroscience, 11(1), 1-12). This shows that people score lower on their first test(s) than they should.
[0004] Practice effects may lead to underestimating the severity of disease progression
or overestimating the efficacy of treatment effects (see for example:
Jutten, R. J., Grandoit, E., Foldi, N. S., Sikkes, S. A., Jones, R. N., Choi, S. E.,
... & Rabin, L. A. (2020). Lower practice effects as a marker of cognitive performance
and dementia risk: a literature review. Alzheimer's & Dementia: Diagnosis, Assessment
& Disease Monitoring, 12(1), e12055). There can be various reasons for practice effects; e.g. people might need time
to understand the instructions, or to translate the instructions into action, or to
reach the peak in their performance.
[0005] Pre-training in order that a person (patient) has reached the plateau take valuable
time, and it can be difficult to determine when the results of the cognitive test
can be properly evaluated.
[0006] There is a need to address these issues.
SUMMARY OF THE INVENTION
[0007] It would be advantageous to provide an improved technique to determine cognitive
performance for a person (patient).
[0008] The object of the present invention is solved with the subject matter of the independent
claims, wherein further embodiments are incorporated in the dependent claims. It should
be noted that the following described aspects and examples of the invention apply
to the cognitive performance determination apparatus, the cognitive performance determination
method, as well as to a computer program element and a computer readable medium,
[0009] In a first aspect, there is provided a cognitive performance determination apparatus,
comprising:
- an input unit; and
- a processing unit.
[0010] The input unit is configured to provide the processing unit with results of a plurality
of trials of a test. The results of each trial is generated by a person undertaking
a plurality of events of each trial. The processing unit is configured to determine
a plurality of performance values for the results of the plurality of trials of the
test. A performance value is determined for each trial of the plurality of trials.
The processing unit is configured to determine information about the person. The determination
of the information about the person comprises a calculation of a curve of a model
fit to at least some of the plurality of performance values and/or the determination
of the information about the person comprises a calculation of performance variability
between at least one consecutive pair of performance values.
[0011] The person can for example be a patient undergoing some form of psychological test.
The person can however be someone other than a patient, for example someone undergoing
a test for acceptance for further education or a person undergoing a test as part
of an application for employment.
[0012] In an example, the determination of the information about the person comprises the
calculation of performance variability between at least one consecutive pair of performance
values. The information about the person can then comprise one or more trials of the
plurality of trials selected on the basis of the performance variability between at
least one consecutive pair of performance values.
[0013] Thus, a test consisting of a number of trials of a fixed logical order of events,
often involving the presentation of a stimulus and processing the response to said
stimulus, undertaken by the patient will show practice effects as the person's performance
gradually increases towards a plateau. Thus, the results of trials that the person
has undertaken after a deviation between trails shows that they are now at a plateau
in performance can be selected, for example for further analysis by a medical professional/clinician
or examination overseer.
[0014] In an example, the determination of the information about the person comprises the
calculation of performance variability between at least one consecutive pair of performance
values. The information about the person can then comprises one or more trials of
the plurality of trials that were undertaken by the person after the performance variability
between a consecutive pair of performance values is equal to or is below a threshold
value.
[0015] Thus, as a person carries out trials of a test their performance starts from a baseline
and gradually improves towards a plateau that is representative of their actual cognitive
capability with respect to the test, and it can then be determined to select those
trials at the plateau that are representative for further analysis by a medical professional
or test overseer, and not utilise the trial results leading up to the plateau.
[0016] In an example, the threshold value is a percentage value and is one of: 1%, 2%, 3%,
4%, 5%, 6%, 7%, 8%, 9%, 10%, 15%, 20%.
[0017] In an example, the determination of the information about the person comprises the
calculation of performance variability between at least one consecutive pair of performance
values. The input unit is configured to provide the processing unit with a performance
variability between at least one consecutive pair of performance values for one or
more further persons generated from results of a plurality of trials of the test undertaken
by the one or more further persons. The information about the person can then comprise
a comparison of the performance variability between the at least one consecutive pair
of performance values for the person with the performance variability between at least
one consecutive pair of performance values for the one or more further persons.
[0018] In other words, there variability between trials of a test with respect to the trials
leading up to a plateau and of the trials at the plateau can be utilised to provide
information on the cognitive ability of the person through comparison of this variability
in performance with that of other persons performing the same or similar test.
[0019] In an example, the determination of the information about the person comprises the
calculation of the curve of the model fit to the at least some of the plurality of
performance values. The information about the person can then comprise information
derived from the curve of the model fit to the at least some of the plurality of performance
values.
[0020] Thus, it has been established that model can be fit to the performance improvement
of a person as they undertake trials of a test as a performance gradually increases
to a plateau. This means that the trials that are at the plateau can be selected for
further analysis by medical professional, and the curve itself can be utilised to
provide information on the cognitive ability of the person such as through timescale
value related to how long it takes the person to reach the plateau. This means, that
cognitive information of the person can be determined from trials where the person
never actually reaches the plateau, providing for an efficient ability to determine
cognitive ability of the person via a minimum number of trials and even for a person
for whom it would take a very very long time to reach the plateau.
[0021] In an example, the information derived from the curve of the model fit to the at
least some of the plurality of performance values comprises one or more trials of
the plurality of trials selected on the basis of the curve of the model fit to the
at least some of the plurality of performance values.
[0022] In other words, the result of the trials at the plateau that are representative of
the person's cognitive ability can be selected for further analysis, and those trial
results leading up to the plateau can be deselected.
[0023] In an example, the information derived from the curve of the model fit to the at
least some of the plurality of performance values comprises a time constant of the
model used to fit the curve of the model to the at least some of the plurality of
performance values.
[0024] In other words, a timescale associated with a model fitted to how a person's performance
with respect to trials of a test gradually increases provides a new and important
additional outcome measure of the neurological assessment that can be provided to
a medical professional/clinician or to an exam overseer. To put this another way,
the trial results tending toward a plateau, that were previously difficult to utilize
in order to help in a neurological assessment, can now be utilized for that neurological
assessment via a timescale representative of how the person's cognitive performance
increases. This means not only can trial results that were not previously useable
be used, but the neurological assessment can be made more accurate.
[0025] In an example, the input unit is configured to provide the processing unit with a
time constant of the model used to fit the curve of the model to a plurality of performance
values for one or more further persons generated from results of a plurality of trials
of the test undertaken by the one or more further persons. The information about the
person can then comprises a comparison of the time constant of the model used to fit
the curve of the model to the at least some of the plurality of performance values
for the person with the time constant of the model used to fit the curve of the model
to the plurality of performance values for one or more further persons.
[0026] Thus, timescale associated with how a person improves with respect to trials of a
test towards a steady state plateau can be utilised to provide information on cognitive
ability of the person, and one mechanism by which this can be assessed as through
comparison to other similar persons carrying out the same or similar tests. This comparison
of time constant can be particularly meaningful when comparing between similar persons
with respect to age, education, nationality, etc.
[0027] In an example, the input unit is configured to provide the processing unit with one
or more curves of the model each fit to a plurality of performance values for one
or more further persons generated from results of a plurality of trials of the test
undertaken by the one or more further persons. The information about the person can
then comprise a comparison of the curve of the model fit to the at least some of the
plurality of performance values for the person with the one or more curves of the
model each fit to a plurality of performance values for one or more further persons.
[0028] Thus, a curve fitted to a model of how a person improves with respect to trials of
a test towards a steady state plateau can be utilised to provide information on cognitive
ability of the patient, and one mechanism by which this can be assessed as through
comparison to other patients carrying out the same or similar tests.
[0029] In other words, the time constant is an important part of the curve definition that
can be utilized for neurological assessment. However, it has been established that
the how the curve is fully defined can provide further information. This means that
the start performance level and the end plateau height - asymptotic level can in addition
to the time constant be used as part of the neurological assessment. Thus, two curves
from different persons can have the same or similar time constants but be different
with respect to the start level and or plateau height, and this further information
can be utilized as part of the neurological assessment.
[0030] In an example, the information derived from the curve of the model fit to the at
least some of the plurality of performance values comprises an asymptotic performance
value of the model used to fit the curve of the model to the at least some of the
plurality of performance values. The information about the person can then comprise
one or more trials of the plurality of trials that were undertaken by the person after
a trial that has a performance value within a threshold value of the asymptotic performance
value of the model.
[0031] In other words, a curve of a model fit the performance of the person undertaking
trials gradually rises towards a plateau in an asymptotic manner, and the asymptotic
value associated with this curve indicates the plateau level. This value can then
be used for example to indicate value of the performance of the person which itself
is an indicator of the cognitive ability of the person which could be compared with
the same value for other persons conducting the same or similar test. This value can
also be used to select the trial results at the plateau that can be utilised for further
analysis, and deselect those trial results leading up to the plateau.
[0032] In an example, the determination of the information about the person comprises the
calculation of the curve of the model fit to the at least some of the plurality of
performance values and the calculation of performance variability between at least
one consecutive pair of performance values. The processing unit is configured to determine
the at least some of the plurality of performance values as the performance values
before a performance value that is one of the consecutive pair performance values
that have a performance variability equal to or below a threshold value.
[0033] Thus, the variability between consecutive trial results can be utilised to determine
when the person has reached a plateau in performance. This can be used to select the
trial results prior to this point that are associated with the gradual increase of
the person's performance. These trial results leading up to the plateau can then be
used to fit the curve of the model, enabling the model to better fit the data and
provide more accurate information relating to this model.
[0034] In an example, the input unit is configured to provide the processing unit with results
of a plurality of trials of a second test. The results of each trial of the second
test is generated by the person undertaking a plurality of events of each trial of
the second test. The processing unit is configured to determine a plurality of performance
values for the results of the plurality of trials of the second test. A performance
value is determined for each trial of the plurality of trials of the second test.
The processing unit is configured to calculate a curve of the model fit to at least
some of the plurality of performance values of the second test and/or to calculate
performance variability between at least one consecutive pair of performance values
of the second test. The information about the person comprises a comparison of the
curve of the model fit to at the least some of the plurality of performance values
of the test with the curve of the model fit to at the least some of the plurality
of performance values of the second test and/or a comparison of the performance variability
between at least one consecutive pair of performance values of the test with the performance
variability between at least one consecutive pair of performance values of the second
test.
[0035] Thus, a person can conduct the same test at different times and the performance of
the person taking this test and how it varies can be utilised to determine changes
in cognitive ability. Alternatively or additionally, the person can undertake tests
associated with different cognitive domains providing for an assessment of different
cognitive abilities with respect to different neuropsychological assessments.
[0036] In a second aspect, there is provided a cognitive performance determination method,
comprising:
providing a processing unit with results of a plurality of trials of a test, wherein
the results of each trial is generated by a person undertaking a plurality of events
of each trial;
determining by the processing unit a plurality of performance values for the results
of the plurality of trials of the test, wherein a performance value is determined
for each trial of the plurality of trials; and
determining by the processing unit information about the person, and wherein the determining
the information about the person comprises calculating a curve of a model fit to at
least some of the plurality of performance values and/or the determining the information
about the person comprises calculating performance variability between at least one
consecutive pair of performance values.
[0037] According to another aspect, there is provided computer program elements controlling
one or more of the apparatuses as previously described which, if the computer program
element is executed by a processor, is adapted to perform the method as previously
described.
[0038] According to another aspect, there is provided computer readable media having stored
the computer elements as previously described.
[0039] The computer program element can for example be a software program but can also be
a FPGA, a PLD or any other appropriate digital means.
[0040] Advantageously, the benefits provided by any of the above aspects equally apply to
all of the other aspects and vice versa.
[0041] The above aspects and examples will become apparent from and be elucidated with reference
to the embodiments described hereinafter.
BRIEF DESCRIPTION OF THE DRAWINGS
[0042] Exemplary embodiments will be described in the following with reference to the following
drawings:
Fig. 1 (by: Bartels, C., Wegrzyn, M., Wiedl, A., Ackermann, V., & Ehrenreich, H. (2010). Practice
effects in healthy adults: a longitudinal study on frequent repetitive cognitive testing.
BMC neuroscience, 11(1), 1-12) shows examples of practice effects;
Fig. 2 shows a schematic representation of a cognitive performance determination apparatus;
Fig. 3 shows a cognitive performance determination method;
Fig. 4 shows an example of a person's performance with respect to taking trials of
a test;
Fig. 5 shows an example of a person's performance with respect to taking trials of
a test;
Fig. 6 shows an example of a person's performance with respect to taking trials of
a test;
Fig. 7 shows an example of a person's performance with respect to taking trials of
a test; and
Fig. 8 shows an example of the elementary building blocks of the new technique.
DETAILED DESCRIPTION OF EMBODIMENTS
[0043] The new technique provides an ability to aid performance assessment in the context
of neuropsychological assessments, however the technique is also applicable to other
cognitive or motor function assessments (e.g. SAT). Thus, such test can also be referred
to as cognitive tests.
[0044] Fig. 2 shows an example of a cognitive performance determination apparatus 10. The
apparatus comprises an input unit 20, and a processing unit 30. The input unit is
configured to provide the processing unit with results of a plurality of trials of
a test, and the results of each trial is generated by a person undertaking a plurality
of events of each trial. The processing unit is configured to determine a plurality
of performance values for the results of the plurality of trials of the test, and
a performance value is determined for each trial of the plurality of trials. The processing
unit is configured to determine information about the person. The determination of
the information about the person comprises a calculation of a curve of a model fit
to at least some of the plurality of performance values. Additionally or alternatively
the determination of the information about the person comprises a calculation of performance
variability between at least one consecutive pair of performance values.
[0045] According to an example, the determination of the information about the person comprises
the calculation of performance variability between at least one consecutive pair of
performance values. The information about the person can then comprise one or more
trials of the plurality of trials selected on the basis of the performance variability
between at least one consecutive pair of performance values.
[0046] According to an example, the determination of the information about the person comprises
the calculation of performance variability between at least one consecutive pair of
performance values. The information about the person can then comprise one or more
trials of the plurality of trials that were undertaken by the person after the performance
variability between a consecutive pair of performance values is equal to or is below
a threshold value.
[0047] According to an example, the threshold value is a percentage value and is one of:
1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 15%, 20%.
[0048] In an example, the input unit is configured to provide the processing unit with the
threshold value input from a user.
[0049] Thus, the default threshold value of for example 5% can be utilised to determine
when the plateau is reached when the variability between consecutive trials is 5%
or less, and a medical professional can adjust this if necessary based on their experience.
[0050] According to an example, the determination of the information about the person comprises
the calculation of performance variability between at least one consecutive pair of
performance values. The input unit is configured to provide the processing unit with
a performance variability between at least one consecutive pair of performance values
for one or more further persons generated from results of a plurality of trials of
the test undertaken by the one or more further persons. The information about the
person can then comprise a comparison of the performance variability between the at
least one consecutive pair of performance values for the person with the performance
variability between at least one consecutive pair of performance values for the one
or more further persons.
[0051] According to an example, the determination of the information about the person comprises
the calculation of the curve of the model fit to the at least some of the plurality
of performance values. The information about the person can then comprise information
derived from the curve of the model fit to the at least some of the plurality of performance
values.
[0052] According to an example, the information derived from the curve of the model fit
to the at least some of the plurality of performance values comprises one or more
trials of the plurality of trials selected on the basis of the curve of the model
fit to the at least some of the plurality of performance values.
[0053] According to an example, the information derived from the curve of the model fit
to the at least some of the plurality of performance values comprises a time constant
of the model used to fit the curve of the model to the at least some of the plurality
of performance values.
[0054] According to an example, the input unit is configured to provide the processing unit
with a time constant of the model used to fit the curve of the model to a plurality
of performance values for one or more further persons generated from results of a
plurality of trials of the test undertaken by the one or more further persons. The
information about the person can then comprise a comparison of the time constant of
the model used to fit the curve of the model to the at least some of the plurality
of performance values for the person with the time constant of the model used to fit
the curve of the model to the plurality of performance values for one or more further
persons.
[0055] According to an example, the input unit is configured to provide the processing unit
with one or more curves of the model each fit to a plurality of performance values
for one or more further persons generated from results of a plurality of trials of
the test undertaken by the one or more further persons. The information about the
person can then comprise a comparison of the curve of the model fit to the at least
some of the plurality of performance values for the person with the one or more curves
of the model each fit to a plurality of performance values for one or more further
persons.
[0056] According to an example, the information derived from the curve of the model fit
to the at least some of the plurality of performance values comprises an asymptotic
performance value of the model used to fit the curve of the model to the at least
some of the plurality of performance values. The information about the person can
then comprise one or more trials of the plurality of trials that were undertaken by
the person after a trial that has a performance value within a threshold value of
the asymptotic performance value of the model.
[0057] In an example, the threshold value is a percentage value and is one of: 1%, 2%, 3%,
4%, 5%, 6%, 7%, 8%, 9%, 10%, 15%, 20%.
[0058] In an example, the input unit is configured to provide the processing unit with the
threshold value input from a user.
[0059] It however to be noted that the performance leading up to the plateau can be utilized
for neurological assessment and thus those trials at the plateau need not be utilized
and indeed the test may not need to proceed until the trail results indicate that
the person has reached the plateau, rather just the trial results during the increase
in performance phase can be utilized for the neurological assessment, through for
example a determination of a characteristic time constant associated with the increase
in performance.
[0060] According to an example, the determination of the information about the person comprises
the calculation of the curve of the model fit to the at least some of the plurality
of performance values and the calculation of performance variability between at least
one consecutive pair of performance values. The processing unit is configured to determine
the at least some of the plurality of performance values as the performance values
before a performance value that is one of the consecutive pair performance values
that have a performance variability equal to or below a threshold value.
[0061] In an example, the threshold value is a percentage value and is one of: 1%, 2%, 3%,
4%, 5%, 6%, 7%, 8%, 9%, 10%, 15%, 20%.
[0062] In an example, the input unit is configured to provide the processing unit with the
threshold value input from a user.
[0063] According to an example, the input unit is configured to provide the processing unit
with results of a plurality of trials of a second test, and the results of each trial
of the second test is generated by the person undertaking a plurality of events of
each trial of the second test. The processing unit is configured to determine a plurality
of performance values for the results of the plurality of trials of the second test,
and a performance value is determined for each trial of the plurality of trials of
the second test. The processing unit is configured to calculate a curve of the model
fit to at least some of the plurality of performance values of the second test and/or
to calculate performance variability between at least one consecutive pair of performance
values of the second test. The information about the person can then comprise a comparison
of the curve of the model fit to at the least some of the plurality of performance
values of the test with the curve of the model fit to at the least some of the plurality
of performance values of the second test and/or a comparison of the performance variability
between at least one consecutive pair of performance values of the test with the performance
variability between at least one consecutive pair of performance values of the second
test.
[0064] In an example, the second test is the same test as the test.
[0065] In an example, the second test is a different test to the test.
[0066] In an example, the test relates to a first cognitive domain and the second test relates
to a second cognitive domain.
[0067] Fig. 3 shows a cognitive performance determination method 100, comprising:
providing 110 a processing unit with results of a plurality of trials of a test, wherein
the results of each trial is generated by a person undertaking a plurality of events
of each trial;
determining 120 by the processing unit a plurality of performance values for the results
of the plurality of trials of the test, wherein a performance value is determined
for each trial of the plurality of trials; and
determining 130 by the processing unit information about the person, and wherein the
determining the information about the person comprises calculating a curve of a model
fit to at least some of the plurality of performance values and/or the determining
the information about the person comprises calculating performance variability between
at least one consecutive pair of performance values.
[0068] In an example, the determining the information about the person comprises the calculating
performance variability between at least one consecutive pair of performance values,
and wherein the information about the person comprises one or more trials of the plurality
of trials selected on the basis of the performance variability between at least one
consecutive pair of performance values.
[0069] In an example, the determining the information about the person comprises the calculating
performance variability between at least one consecutive pair of performance values,
and wherein the information about the person comprises one or more trials of the plurality
of trials that were undertaken by the person after the performance variability between
a consecutive pair of performance values is equal to or is below a threshold value.
[0070] In an example, the threshold value is a percentage value and is one of: 1%, 2%, 3%,
4%, 5%, 6%, 7%, 8%, 9%, 10%, 15%, 20%.
[0071] In an example, the method comprises providing the processing unit with the threshold
value input from a user.
[0072] In an example, the determining the information about the person comprises the calculating
the curve of the model fit to the at least some of the plurality of performance values
and the calculating performance variability between at least one consecutive pair
of performance values, wherein the method comprises providing the processing unit
with a performance variability between at least one consecutive pair of performance
values for one or more further persons generated from results of a plurality of trials
of the test undertaken by the one or more further persons, and wherein the information
about the person comprises comparing the performance variability between the at least
one consecutive pair of performance values for the person with the performance variability
between at least one consecutive pair of performance values for the one or more further
persons.
[0073] In an example, the determining the information about the person comprises the calculating
the curve of the model fit to the at least some of the plurality of performance values,
and wherein the information about the person comprises information derived from the
curve of the model fit to the at least some of the plurality of performance values.
[0074] In an example, the information derived from the curve of the model fit to the at
least some of the plurality of performance values comprises one or more trials of
the plurality of trials selected on the basis of the curve of the model fit to the
at least some of the plurality of performance values.
[0075] In an example, the information derived from the curve of the model fit to the at
least some of the plurality of performance values comprises a time constant of the
model used to fit the curve of the model to the at least some of the plurality of
performance values.
[0076] In an example, the method comprises providing the processing unit with a time constant
of the model used to fit the curve of the model to a plurality of performance values
for one or more further persons generated from results of a plurality of trials of
the test undertaken by the one or more further persons, and wherein the information
about the person comprises comparing the time constant of the model used to fit the
curve of the model to the at least some of the plurality of performance values for
the person with the time constant of the model used to fit the curve of the model
to the plurality of performance values for one or more further persons.
[0077] In an example, the method comprises providing the processing unit with one or more
curves of the model each fit to a plurality of performance values for one or more
further persons generated from results of a plurality of trials of the test undertaken
by the one or more further persons, and wherein the information about the person comprises
comparing the curve of the model fit to the at least some of the plurality of performance
values for the person with the one or more curves of the model each fit to a plurality
of performance values for one or more further persons.
[0078] In an example, the information derived from the curve of the model fit to the at
least some of the plurality of performance values comprises an asymptotic performance
value of the model used to fit the curve of the model to the at least some of the
plurality of performance values, and wherein the information about the person comprises
one or more trials of the plurality of trials that were undertaken by the person after
a trial that has a performance value within a threshold value of the asymptotic performance
value of the model.
[0079] In an example, the threshold value is a percentage value and is one of: 1%, 2%, 3%,
4%, 5%, 6%, 7%, 8%, 9%, 10%, 15%, 20%.
[0080] In an example, the input unit is configured to provide the processing unit with the
threshold value input from a user.
[0081] In an example, the determining the information about the person comprises the calculating
the performance variability between at least one consecutive pair of performance values,
and wherein the method comprises determining by the processing unit the at least some
of the plurality of performance values as the performance values before a performance
value that is one of the consecutive pair performance values that have a performance
variability equal to or below a threshold value.
[0082] In an example, the threshold value is a percentage value and is one of: 1%, 2%, 3%,
4%, 5%, 6%, 7%, 8%, 9%, 10%, 15%, 20%.
[0083] In an example, the method comprises providing the processing unit with the threshold
value input from a user.
[0084] In an example, the method comprises providing the processing unit with results of
a plurality of trials of a second test, wherein the results of each trial of the second
test is generated by the person undertaking a plurality of events of each trial of
the second test; wherein the method comprises determining by the processing unit a
plurality of performance values for the results of the plurality of trials of the
second test, wherein a performance value is determined for each trial of the plurality
of trials of the second test, wherein the method comprises calculating by the processing
unit a curve of the model fit to at least some of the plurality of performance values
of the second test and/or calculating by the processing unit performance variability
between at least one consecutive pair of performance values of the second test, and
wherein the information about the person comprises comparing the curve of the model
fit to at the least some of the plurality of performance values of the test with the
curve of the model fit to at the least some of the plurality of performance values
of the second test and/or comparing the performance variability between at least one
consecutive pair of performance values of the test with the performance variability
between at least one consecutive pair of performance values of the second test.
[0085] In an example, the second test is the same test as the test.
[0086] In an example, the second test is a different test to the test.
[0087] In an example, the test relates to a first cognitive domain and the second test relates
to a second cognitive domain.
[0088] Thus, as pre-training takes valuable time, the new technique has been developed that
for example has the benefit to estimate the plateau of the performance and time to
reach it without executing a very large number of trials.
[0089] Thus, the new technique detects a performance plateau during neurophysiological assessments
using performance data. This can be used to quantify practice effects and interpret
these by comparing to a normalized average, longitudinal assessments or between cognitive
domains. This leads to several advantages:
More reliable and better results by considering only datapoints when a performance
plateau is reached.
[0090] Increased sensitivity in testing as time to reach performance plateau can serve as
additional measure in a neuropsychological assessment.
[0091] The cognitive performance determination apparatus and the cognitive performance determination
method are now described in further specific detail, where reference is made to Figs,
4-8.
[0092] Typically practice effects are viewed as a source of bias in repeated neuropsychological
assessments.
[0093] However, it was realized that they can also provide additional information about
the person's cognitive capabilities, and this led to the development of the new cognitive
performance determination apparatus and the cognitive performance determination method
described here.
[0094] Thus, if a person takes longer to reach a performance plateau this may be an early
indication of a change in cognitive functioning. In disorders where learning is compromised,
such as Alzheimer's disease (AD) or mild cognitive impairment (MCI), attenuated practice
effects can be expected. In general or on specific tasks - e.g., lower practice effects
on episodic memory tasks have been related to AD dementia (see for example:
Jutten, R. J., Grandoit, E., Foldi, N. S., Sikkes, S. A., Jones, R. N., Choi, S. E.,
... & Rabin, L. A. (2020). Lower practice effects as a marker of cognitive performance
and dementia risk: a literature review. Alzheimer's & Dementia: Diagnosis, Assessment
& Disease Monitoring, 12(1), e12055). In other words; variability in practice effects can be used to define cases or
predict longitudinal outcomes.
[0095] Thus, in the new technique described here providing a comparison of learning curves
to a normalized average, between longitudinal assessments, or between cognitive domains
can therefore provide important additional information on the change in a person's
neuropsychology. Therefore, a method to detect, quantify and compare these practice
effects was developed to enable these beneficial effects.
[0096] Typically, each neuropsychological assessment (or test battery) consists of number
of neuropsychological tests (e.g. RAVLT, TMT, etc.). In each test the person is presented
with a number of items and a logical sequence of (presenting and processing responses
to) items is called a trial. Some tests can have only one trial. An assumption was
made in the new development that after each trial the next one will be executed faster
or with less errors. It was then taken that this general learning process will continue
until hardly any improvement is observable anymore: the performance plateau is reached.
It was then determined in the new technique that this general learning process can
be described with the following model formula:

[0097] Where:
- t
- = a numeric vector that represents trial number 1, 2,..
- Asym
- = a numeric parameter representing the upper asymptotic value of the model
- Perf0
- = a numeric parameter representing the performance at t=0 (i.e., before training)
- tau
- = a numeric parameter representing the learning constant
[0098] The relative error between succeeding trials can be defined by the following formula:

[0099] Applying these two formulas to trial data the performance plateau and the time to
reach it can be estimated without executing a very large number of training sessions.
[0100] For people with a small
tau - reaching their performance plateau after only a few trials - the relative error
between two succeeding training trails can be sufficient to determine their plateau.
That is if the difference in performance on trial
i vs trial
i-l is smaller than for instance 5% the person has reached a plateau in performance.
There can be multiple explanations for a small tau including pre (athome) training,
having done a similar assessment recently, or high cognitive function.
[0101] For people with a larger
tau - reaching their performance plateau after >3 trials - a plateau in performance can
be estimated by fitting a non-linear model to the performance data (see for example:
Venables, W. N., & Ripley, B. D. (2003). Modern Applied Statistics With S. (J. Chambers,
W. Eddy, W. Härdle, S. Sheather, & L. Tierney, Eds.), Technometrics (Fourth Edi, Vol.
45). New York, NY Springer Science+Business Media). Considering the 3 parameters of the model formula equation above, data of at least
4 trails are needed. Each additional trial will make the estimate more reliable.
[0102] Thus, in a specific embodiment of the new technique the first step is assessing the
patient's variability between consecutive trials for <3 trials. If the variability
between these trials is smaller than for instance 5% it can be safely assumed that
the patient is on their performance plateau. Thus, fitting the performance data to
a curve is not needed. However, if the variability in the first 3 trials is larger
than for instance 5% more trials are needed, and the patient's performance should
be fitted to a general learning curve to estimate plateau. Using these two formulas
the performance plateau can be estimated regardless of any pretraining of the patient.
It is to be noted that a threshold of 5% can be set initially, as this is a common
threshold used in statistics, and the clinician can re-evaluate this threshold at
her/his own discretion and increase it or reduce it as necessary.
[0103] It is to be noted that by fitting the performance data to a curve the plateau can
be predicted before it is reached. This feature can be especially valuable for patients
that need a very large number of trials to reach their plateau. In this case a reliable
estimated of learning ability and performance can be provided without the need to
complete all trials. Thus, a large amount of time is saved. Also, by fitting the performance
data to a curve, differences in the learning curve between patients or for the same
patient over time can be detected, where the difference in the curves can be represented
by differences in tau. This difference in tau can be interpreted using contextual
knowledge such as time between testing, degeneration on MRI niveau, age, and task
complexity.
[0104] Figs. 4-7 then show some illustrative examples, showing cases that differ in terms
of how fast the person learns
(tau: that is the time to reach the plateau). In the figures
tst stands for performance and t for the number of trials.
[0105] In Fig. 4, this person reached a plateau in performance after 2 trials because the
relative error between trial 1 and 2 is smaller than 5% (2.8%).
[0106] In Fig. 5, this person reached a plateau in performance after 4 trials, because the
relative error between trial 3 and 4 is dropped below 5% (0.39%). Because the person
already executed 4 trials a non-linear fit with the learning model can be performed
to give an estimations and error margin of the asymptotic value (
p < 0.05),
tau (
p > 0.1) and response at time 0 (
p > 0.1).
[0107] In Fig, this person reached a plateau in performance after 5 g trials, because the
relative performance dropped below 5% between trial 4 and 5 (2.0%). The non-linear
model can use now 5 data points to give more accurate parameter estimations.
[0108] In Fig. 7, this person reached a plateau in performance after 7 trials, because the
relative performance dropped below 5% between trial 6 and 7 (1.7%). The non-linear
model can use now 7 data points to give more accurate parameter estimations.
[0109] As discussed above, the new technique involves 1. fitting performance data to a general
learning curve, and/or 2. considering the relative error between trials as an indicator
of performance plateau.
[0110] Here, performance can be fitted to a general learning curve, one neuropsychological
assessment can consist of one or multiple tests, with each test consisting of multiple
trials, and here a longitudinal assessment is comparing assessments over time.
[0111] In a complete system that makes use of the new technique (the cognitive performance
determination apparatus and the cognitive performance determination method), shown
schematically in Fig. 8. In Fig. 8 the first box A represents "presentation" with
an interface that presents sensory training stimuli to a patient, the second box B
represents "Acquisition" to acquire performance data, the third box C represents "Processing"
with a processor to execute the described new cognitive performance determination
apparatus and a cognitive performance determination method/algorithm related to the
performance points and plateau determination, and box D represents "Recommendation"
with a unit to log/store or present data related to reaching the plateau.
[0112] Thus, in such an exemplar complete system the following can be utilized:
An interface/unit that presents sensory training stimuli to a patient.
Measuring/sensor unit to acquire performance data;
A processor to execute an algorithm that
Receives data from the measuring unit.
Optionally, receives the minimum and maximum performance score for that test.
Determines the occurrence of a performance plateau, which is when
[0113] The relative error between the first and the second trial is smaller than for instance
5% using

[0114] OR when after 4 or more trials the non-linear model estimates learning parameters
Asym, Per0 and
tau, using

and stops when last performance was within for instance 5% of estimated
Asym
[0115] Then, training stops automatically, and the last trials (close enough to the plateau)
count for the assessment or the learning model is used to correct for the missed performance
in the previous trials. This correction relates to the case of someone that practiced
the test before or a set up in which data for a specific trial is missed/lost. Using
the model we can determine that a person has encountered the test before, because
their start performance (Perf0 in the model) is too high and in effect an estimation
can be made of the trials with missing data as the curve would pass through the associated
performance points for such missing data.
[0116] Unit to log/store or present data related the occurrence of a performance plateau.
[0117] The following describes the cognitive performance determination apparatus and the
cognitive performance determination method via a discussion of several exemplar embodiments.
First Embodiment
[0118] The first embodiment can be used to detect a learning plateau and quantify the practice
effect during a neuropsychological test. This embodiment has elements as described
above. Preferably the assessment is giving on a digital platform, such as IntelliSpace
Cognition. This embodiment can be applied to any type of neuropsychological test as
long as the performance data can be captured and processed during or immediately after
the test (e.g. the SDMT, Star Cancelation, STROOP) and the test consists of multiple
trials (or enough versions of the test with the same difficulty level are available).
The performance data is to be processed immediately after the trial is finished. Alternatively,
the performance data is available for processing in real-time (then performance data
may need to be normalized or it may be beneficial to use an average performance over
a set time, or an average within a moving window). A performance plateau is detected
using the formulas as described above. Optionally, a display unit to show the measured
and processed signals to the assessor, indicates when a performance plateau is reached
or stops the training. The last trial is within the plateau and can optionally count
for the assessment.
Second Embodiment
[0119] The second embodiment can consist of all elements of the first embodiment and additionally
compares the time it takes to reach a plateau in performance on a particular test
between consecutive assessments of a person (i.e. longitudinal assessment). This comparison
informs on how good the person (patient) holds on to the capabilities to perform that
test. The relevant parameter here is tau which is a numeric parameter representing
the learning constant. When a test is new, a person needs time to learn it - so the
first assessment has the largest tau. Any time after that it is expected that tau
is shorter, except if the time between the two assessment is long enough to unlearn
the test (this depends on e.g., the type of test and characteristics of the respondent).
If during an assessment the tau is similar or longer than in the preceding assessment,
this may indicate a deterioration in a part of cognitive function. Optionally one
may correct this comparison for the time between tests (parts of the test are more
likely to be remembered when the time between tests is shorter than longer, which
affect the practice effect). Optionally, one may inspect the comparison of learning
curves between assessment for one domain and contrast this to a second domain. This
comparison of learning curve between assessments can provide valuable insight on the
cognitive ability of the person, in general or for specific cognitive domains. Here,
reference to domains refers to cognitive domains. Domains can for example be 'Working
Memory' or Executive Function'. Their definition depends upon the model that is used.
(see for example:
S. Vermeent et al: Evidence of Validity for a Newly Developed Digital Cognitive Test
Battery, Frontiers in Psychology, 11, 2020, and
S. Vermeent et al: Philips Intellispace Cognition digital test battery: equivalence
and measurement invariance compared to traditional analog test versions, The clinical
Neuropsychologist 2021.). Each domain can be assessed with specific tests. Therefore to get an overview
of someone's cognitive abilities, neuropsychological assessments consist of multiple
tests assessing several cognitive domains.
[0120] Thus, a comparison between curves of the model for the patient doing the same test
at different times. It is common practice that one patient does a neuropsychological
assessment (NPA) consisting of the same test several times in their lifetime. During
each NPA using the new technique a general tau can be calculated - that is an average
learning curve for all tests in the NPA. Also, a domain-specific tau can be calculated
- that is an average learning curve for a set of tests that correspond to a cognitive
domain. Also a test specific tau can be calculated - that is a tau for a specific
test. These three types of taus can then be compared between the neuropsychological
assessments that a patient completed in their life to provide information on for example
cognitive impairment or decline.
Third embodiment
[0121] The third embodiment can consist of all elements of the first or second embodiment
and additionally compares the practice effects on each test in an assessment to a
normalized population average. Specifically, tau is compared to taus of similar cases.
If a tau is considerably larger than the population average - for the whole assessment,
between parts of the assessment (beginning vs ending), on a specific group of tests,
or on a specific test - but performance is within a normal range, the deviation of
tau may hint at an issue. E.g. but not limited to:
- For the whole assessment or between parts of the assessment (e.g. beginning vs ending):
If a person is generally slower, this can provide information about procedural learning,
for example, or translating the instructions into an action. This kind of information
can be valuable to the assessor.
- On a specific group of tests representing a particular cognitive domain: Domains are
for example working memory, processing speed, verbal processing, executive functioning,
visual spatial processing, etc. This comparison can give an early indication of cognitive
deficiencies on that domain which might be linked to a particular diagnosis.
- On a specific test: If a person has a larger tau than average on a particular test
this could for example hint at a specific abnormality in an underlying cognitive function,
or incorrect assessment of that test.
[0122] Any of these deviations of tau to a normalized population average may be utilized
to make a reference for further inspection.
Fourth Embodiment
[0123] The fourth embodiment is consistent with all previous embodiments. In this embodiment
an estimate of performance in the beginning of the task (t=0), is used to detect if
a patient had prior experience with the task (prior learning). When the model can
estimate a learning curve reliably, performance at the intercept (t=0 - Here Perf0
as one of the model parameters) is contrasted with a distribution of performance estimated
from task-naive patients. When the estimate at t=0, is more than one standard deviation
above the norm, the test instance can be marked as "likely had prior experience".
This warning can be used in data analyses to correct results or provide context. Also,
it could trigger actions during the test battery. In the SDMT, this warning could
trigger actions in the clinical workflow. E.g. suggest to the test administer to ask
a question: "Do you use an application to train your cognition?". Similarly, for the
RAVLT, if performance at t=0 is unreasonably high, a different question is suggested:
"Did you see this list of words during an earlier visit?" The normed estimate of performance
at t=0 can be corrected for demographic (e.g. age, gender, education) and/or clinical
factors. In case of test that are designed for longitudinal administration (multiple
tests), a distinct norm distribution could be used at visit 2, to detect if patients
have started training on a task between visit 1 and 2. Besides the intercept at t=0,
other metrics to estimate prior learning can be used or combined to estimate prior
experience with a task. For example, the first derivate/slope over the initial trials,
task-specific parameters related to response times, or the amount of time spend listening
to instructions or on practice trials. In certain tests response time is part of the
assessment, especially tasks that target the cognitive domain 'processing speed' such
as Trail-Making Test A, or the Stroop color-naming test, and here the specific parameters
that relate to response time differ per test.
[0124] A number of acronyms have been used above, details of these are as follows:
SAT: Scholastic Assessment/Aptitude Test; a standardized test widely used for college
admissions in the US.
RAVLT; Rey Auditory Verbal Learning Test (RAVLT); a neuropsychological test assessing
(Working/Learning) Memory. Rey Auditory Verbal Learning Test.docx (live.com)
TMT: Trail Making Test is a neuropsychological test assessing working memory and executive
functioning. It consists of two parts: in part A the patient Is asked to connect 25
circled numbers in numerical order, in part B the patient connects 25 encircled numbers
and letters in numerical and alphabetical order alternating.
[0125] Star cancellation: is a neuropsychological test developed to detect the presence
of unilateral spatial neglect. A participant is required to search for and cross out
("cancel") targets, which are usually embedded among distractor stimuli. The number
of cancelled targets and their location can be used to diagnose the neglect syndrome
after stroke.
[0126] STROOP: The Stroop task is a neuropsychological test assessing executive functioning
and processing speed. It requires individuals to view a list of words that are printed
in a different color than the meaning of the word. Participants are tasked with naming
the color of the word, not the word itself, as fast as they can.
[0127] SDMT: Symbol Digit Modalities test is a neuropsychological test assesses neurological
dysfunction. A patient is given a key of numbers corresponding to a set of figures.
They then are asked to 'translate' a set of figures by assigning the correct number
to each figure using the key.
[0128] PPG: Photoplethysmography (PPG) is an uncomplicated and inexpensive optical measurement
method that is often used for heart rate monitoring purposes. It measures pulse using
reflection of a red or green light.
[0129] EDA sensor: Electrodermal activity (EDA) refers to skin conductance or sweating.
It is often assessed using two biosignals SCL and number of SCRs.
[0130] SCL: Skin conductance level is the tonic level of skin conductance.
[0131] Number of SCRs: Skin conductance responses are tiny phasic components on top of the
slowly moving skin conductance level. The number of SCRs within a certain timeframe
can be related to sympathetic nervous system activity.
[0132] Decrease in HRV: HRV stands for Heart Rate Variability. This is a biosignal. It refers
to the time between two consecutive heart beats. The variability in these timings
relate to nervous system activity (less time between beats - low HRV - relates to
more sympathetic arousal).
[0133] In another exemplary embodiment, a computer program or computer program element is
provided that is characterized by being configured to execute the method steps of
any of the methods according to one of the preceding embodiments, on an appropriate
apparatus or system.
[0134] The computer program element might therefore be stored on a computer unit, which
might also be part of an embodiment. This computing unit may be configured to perform
or induce performing of the steps of the method described above. Moreover, it may
be configured to operate the components of the above described system. The computing
unit can be configured to operate automatically and/or to execute the orders of a
user. A computer program may be loaded into a working memory of a data processor.
The data processor may thus be equipped to carry out the method according to one of
the preceding embodiments.
[0135] This exemplary embodiment of the invention covers both, a computer program that right
from the beginning uses the invention and computer program that by means of an update
turns an existing program into a program that uses the invention.
[0136] Further on, the computer program element might be able to provide all necessary steps
to fulfill the procedure of an exemplary embodiment of the method as described above.
[0137] According to a further exemplary embodiment of the present invention, a computer
readable medium, such as a CD-ROM, USB stick or the like, is presented wherein the
computer readable medium has a computer program element stored on it which computer
program element is described by the preceding section.
[0138] A computer program may be stored and/or distributed on a suitable medium, such as
an optical storage medium or a solid state medium supplied together with or as part
of other hardware, but may also be distributed in other forms, such as via the internet
or other wired or wireless telecommunication systems.
[0139] However, the computer program may also be presented over a network like the World
Wide Web and can be downloaded into the working memory of a data processor from such
a network. According to a further exemplary embodiment of the present invention, a
medium for making a computer program element available for downloading is provided,
which computer program element is arranged to perform a method according to one of
the previously described embodiments of the invention.
[0140] It has to be noted that embodiments of the invention are described with reference
to different subject matters. In particular, some embodiments are described with reference
to method type claims whereas other embodiments are described with reference to the
device type claims. However, a person skilled in the art will gather from the above
and the following description that, unless otherwise notified, in addition to any
combination of features belonging to one type of subject matter also any combination
between features relating to different subject matters is considered to be disclosed
with this application. However, all features can be combined providing synergetic
effects that are more than the simple summation of the features.
[0141] While the invention has been illustrated and described in detail in the drawings
and foregoing description, such illustration and description are to be considered
illustrative or exemplary and not restrictive. The invention is not limited to the
disclosed embodiments. Other variations to the disclosed embodiments can be understood
and effected by those skilled in the art in practicing a claimed invention, from a
study of the drawings, the disclosure, and the dependent claims.
[0142] In the claims, the word "comprising" does not exclude other elements or steps, and
the indefinite article "a" or "an" does not exclude a plurality. A single processor
or other unit may fulfill the functions of several items re-cited in the claims. The
mere fact that certain measures are re-cited in mutually different dependent claims
does not indicate that a combination of these measures cannot be used to advantage.
Any reference signs in the claims should not be construed as limiting the scope.
1. A cognitive performance determination apparatus (10), comprising:
- an input unit (20); and
- a processing unit (30);
wherein the input unit is configured to provide the processing unit with results of
a plurality of trials of a test, wherein the results of each trial is generated by
a person undertaking a plurality of events of each trial;
wherein the processing unit is configured to determine a plurality of performance
values for the results of the plurality of trials of the test, wherein a performance
value is determined for each trial of the plurality of trials; and
wherein the processing unit is configured to determine information about the person,
and wherein the determination of the information about the person comprises a calculation
of a curve of a model fit to at least some of the plurality of performance values
and/or the determination of the information about the person comprises a calculation
of performance variability between at least one consecutive pair of performance values.
2. Apparatus according to claim 1, wherein the determination of the information about
the person comprises the calculation of performance variability between at least one
consecutive pair of performance values, and wherein the information about the person
comprises one or more trials of the plurality of trials selected on the basis of the
performance variability between at least one consecutive pair of performance values.
3. Apparatus according to any of claims 1-2, wherein the determination of the information
about the person comprises the calculation of performance variability between at least
one consecutive pair of performance values, and wherein the information about the
person comprises one or more trials of the plurality of trials that were undertaken
by the person after the performance variability between a consecutive pair of performance
values is equal to or is below a threshold value.
4. Apparatus according to claim 3, wherein the threshold value is a percentage value
and is one of: 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 15%, 20%.
5. Apparatus according to any of claims 1-4, wherein the determination of the information
about the person comprises the calculation of performance variability between at least
one consecutive pair of performance values, wherein the input unit is configured to
provide the processing unit with a performance variability between at least one consecutive
pair of performance values for one or more further persons generated from results
of a plurality of trials of the test undertaken by the one or more further persons,
and wherein the information about the person comprises a comparison of the performance
variability between the at least one consecutive pair of performance values for the
person with the performance variability between at least one consecutive pair of performance
values for the one or more further persons.
6. Apparatus according to any of claims 1-5, wherein the determination of the information
about the person comprises the calculation of the curve of the model fit to the at
least some of the plurality of performance values, and wherein the information about
the person comprises information derived from the curve of the model fit to the at
least some of the plurality of performance values.
7. Apparatus according to claim 6, wherein the information derived from the curve of
the model fit to the at least some of the plurality of performance values comprises
one or more trials of the plurality of trials selected on the basis of the curve of
the model fit to the at least some of the plurality of performance values.
8. Apparatus according to any of claims 6-7, wherein the information derived from the
curve of the model fit to the at least some of the plurality of performance values
comprises a time constant of the model used to fit the curve of the model to the at
least some of the plurality of performance values.
9. Apparatus according to claim 8, wherein the input unit is configured to provide the
processing unit with a time constant of the model used to fit the curve of the model
to a plurality of performance values for one or more further persons generated from
results of a plurality of trials of the test undertaken by the one or more further
persons, and wherein the information about the person comprises a comparison of the
time constant of the model used to fit the curve of the model to the at least some
of the plurality of performance values for the person with the time constant of the
model used to fit the curve of the model to the plurality of performance values for
one or more further persons.
10. Apparatus according to any of claims 6-9, wherein the input unit is configured to
provide the processing unit with one or more curves of the model each fit to a plurality
of performance values for one or more further persons generated from results of a
plurality of trials of the test undertaken by the one or more further persons, and
wherein the information about the person comprises a comparison of the curve of the
model fit to the at least some of the plurality of performance values for the person
with the one or more curves of the model each fit to a plurality of performance values
for one or more further persons.
11. Apparatus according to any of claims 6-10, wherein the information derived from the
curve of the model fit to the at least some of the plurality of performance values
comprises an asymptotic performance value of the model used to fit the curve of the
model to the at least some of the plurality of performance values, and wherein the
information about the person comprises one or more trials of the plurality of trials
that were undertaken by the person after a trial that has a performance value within
a threshold value of the asymptotic performance value of the model.
12. Apparatus according to any of claims 1-11, wherein the determination of the information
about the person comprises the calculation of the curve of the model fit to the at
least some of the plurality of performance values and the calculation of performance
variability between at least one consecutive pair of performance values, and wherein
the processing unit is configured to determine the at least some of the plurality
of performance values as the performance values before a performance value that is
one of the consecutive pair performance values that have a performance variability
equal to or below a threshold value.
13. Apparatus according to any of claims 1-12, wherein the input unit is configured to
provide the processing unit with results of a plurality of trials of a second test,
wherein the results of each trial of the second test is generated by the person undertaking
a plurality of events of each trial of the second test; wherein the processing unit
is configured to determine a plurality of performance values for the results of the
plurality of trials of the second test, wherein a performance value is determined
for each trial of the plurality of trials of the second test, wherein the processing
unit is configured to calculate a curve of the model fit to at least some of the plurality
of performance values of the second test and/or to calculate performance variability
between at least one consecutive pair of performance values of the second test, and
wherein the information about the person comprises a comparison of the curve of the
model fit to at the least some of the plurality of performance values of the test
with the curve of the model fit to at the least some of the plurality of performance
values of the second test and/or a comparison of the performance variability between
at least one consecutive pair of performance values of the test with the performance
variability between at least one consecutive pair of performance values of the second
test.
14. A cognitive performance determination method (100), comprising:
providing (110) a processing unit with results of a plurality of trials of a test,
wherein the results of each trial is generated by a person undertaking a plurality
of events of each trial;
determining (120) by the processing unit a plurality of performance values for the
results of the plurality of trials of the test, wherein a performance value is determined
for each trial of the plurality of trials; and
determining (130) by the processing unit information about the person, and wherein
the determining the information about the person comprises calculating a curve of
a model fit to at least some of the plurality of performance values and/or the determining
the information about the person comprises calculating performance variability between
at least one consecutive pair of performance values.
15. A computer program element for controlling an apparatus according to any of claims
1-13 which when executed by a processor is configured to carry out the method of claim
13.